def test_container(self):
"""FlowgramContainerArray works as expectected"""
fc = FlowgramCollection({"a": "1.0 0.0 0.0 1.0 1.0 1.2 1.2 0.8", "b": "1.2 1.0 0.0 0.8 1.2 2.4 1.0 0.0"})
f_container = FlowgramContainerArray(header)
for f in fc:
f_container.add(f)
for f_obs, f_exp in zip(f_container, fc):
self.assertEqual(str(f_obs), str(f_exp))
def test_container(self):
"""FlowgramContainerArray works as expectected"""
fc = FlowgramCollection({'a':'1.0 0.0 0.0 1.0 1.0 1.2 1.2 0.8',
'b':'1.2 1.0 0.0 0.8 1.2 2.4 1.0 0.0'})
f_container = FlowgramContainerArray(header)
for f in fc:
f_container.add(f)
for f_obs, f_exp in zip(f_container,fc):
self.assertEqual(str(f_obs), str(f_exp))
def test_get_flowgram_distances(self):
"""get_flowgram_distances compute the correct alignment score."""
scores, names, fc = get_flowgram_distances("1", self.flowgram, self.flowgrams,
FlowgramContainerArray(),
{"FZTHQMS01CIW5N":""}, "/tmp/")
self.assertEqual(names, ["FZTHQMS01CIW5N"])
self.assertFloatEqual(scores, [4.95274923, 0.7815385])
def test_get_flowgram_distances_on_cluster(self):
"""get_flowgram_distances_on_cluster computes the correct alignment score."""
self.tmp_dir = mkdtemp(dir="./", suffix="/")
# setup server and workers
self.socket = setup_server()
workers, client_sockets = setup_workers(1,
self.tmp_dir,
self.socket,
verbose=False)
client_sockets = [a for a, b in client_sockets]
scores, names, fc = get_flowgram_distances_on_cluster(
"1", self.flowgram, self.flowgrams, FlowgramContainerArray(),
{"FZTHQMS01CIW5N": ""}, 1, 1, [1], client_sockets)
stop_workers(client_sockets)
self.assertEqual(names, ["FZTHQMS01CIW5N"])
assert_almost_equal(scores, [[4.95274923, 0.7815385]], decimal=4)
def filter_with_flowgram(id,
flowgram,
flowgrams,
header,
ids,
num_flows,
bestscores,
log_fh,
outdir="/tmp/",
threshold=3.75,
num_cpus=32,
fast_method=True,
on_cluster=False,
mapping=None,
spread=[],
verbose=False,
pair_id_thresh=0.97,
client_sockets=[],
error_profile=DENOISER_DATA_DIR +
'FLX_error_profile.dat'):
"""Filter all files in flows_filename with flowgram and split according to threshold.
id: The flowgram identifier of the master flowgram of this round
flowgram: This flowgram is used to filter all the other flowgrams
flowgrams: iterator containing the flowgrams to be filtered
header: a valid sff.txt header
ids: this list marks the active flowgrams, i.e. flowgrams that are unclustered
num_flows: Number of flows remaining in the current round
bestscores: dictionary that stores for each unclustered flowgram the best
score it has to to one of the centroids previously seen
and the id of the centroid. Used in the second denoising phase.
outdir: directory where intermediate and result files go
threshold: Filtering threshold
num_cpus: number of cpus to run on, if on_cluster == True
fast_method: Boolean value for fast denoising with lots of memory
on_cluster: Boolean flag for local vs cluster
mapping: the current cluster mapping
spread: worker processing throughput
error_profile: Path to error profile *.dat file
Implementation detail:
The iterator behind 'flowgrams' is big and thus we want to keep its traversals
at a minimum. The naive implementation of this filter function would traverse the
iterator once to create the input file for the alignment routine, then a second
time to do the actual filtering. To get rid of the second run through the iterator,
we keep a list (in fact a dict) of active 'ids' and do the filtering only in the next
round. A cleaner but still fast solution would be great, as this definitly poses a
pitfall for future modifications.
Returns filename of file containing all non-filtered flows and the number of flows
"""
if verbose:
log_fh.write("Filtering with %s: %d flowgrams\n" % (id, num_flows))
# set up the flowgram storage
if (not fast_method):
fc = FlowgramContainerFile(header, outdir)
else:
fc = FlowgramContainerArray()
# calculate distance scores
if on_cluster:
(scores, names, flowgrams) =\
get_flowgram_distances_on_cluster(
id, flowgram, flowgrams, fc, ids, num_cpus,
num_flows, spread=spread, client_sockets=client_sockets)
else:
(scores, names, flowgrams) =\
get_flowgram_distances(
id, flowgram, flowgrams, fc, ids, outdir=outdir,
error_profile=error_profile)
# shortcut for non-matching flowgrams
survivors = filter(
lambda a_b: a_b[0] < threshold or a_b[1] >= pair_id_thresh, scores)
if (len(survivors) == 0):
# put it in its own cluster
# and remove it from any further searches
if (id in bestscores):
del (bestscores[id])
del (ids[id])
return (flowgrams, num_flows - 1)
# Do the filtering
non_clustered_ctr = 0
for ((score, pair_id), name) in zip(scores, names):
if (score < threshold or name == id or pair_id >= pair_id_thresh):
# make sure the original flowgram gets into this cluster
del (ids[name])
if (name in bestscores):
del (bestscores[name])
if (id != name):
# update the mapping information
mapping[id].extend(mapping[name])
mapping[id].append(name)
# delete the old cluster from the mapping
del (mapping[name])
else:
non_clustered_ctr += 1
# keep track of the best match of this guy to any centroid
if (name not in bestscores or score < bestscores[name][1]):
bestscores[name] = (id, score)
# Some extra safety that we are not missing anything
if (len(ids) != non_clustered_ctr or len(bestscores) != non_clustered_ctr):
raise ApplicationError("filterWithFlowgram failed")
return (flowgrams, non_clustered_ctr)